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7 papers · 2 filters
Posterior Sampling for Large Scale Reinforcement Learning
Georgios Theocharous, Zheng Wen, Yasin Abbasi-Yadkori +1
We propose a practical non-episodic PSRL algorithm that unlike recent state-of-the-art PSRL algorithms uses a deterministic, model-independent episode switching schedule. Our algor…
Robust Locally-Linear Controllable Embedding
Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh +2
Embed-to-control (E2C) is a model for solving high-dimensional optimal control problems by combining variational auto-encoders with locally-optimal controllers. However, the E2C mo…
SpectralLeader: Online Spectral Learning for Single Topic Models
Tong Yu, Branislav Kveton, Zheng Wen +2
We study the problem of learning a latent variable model from a stream of data. Latent variable models are popular in practice because they can explain observed data in terms of un…
A Tutorial on Thompson Sampling
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni +2
Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immed…
Introspection: Accelerating Neural Network Training By Learning Weight Evolution
Abhishek Sinha, Mausoom Sarkar, Aahitagni Mukherjee +1
Neural Networks are function approximators that have achieved state-of-the-art accuracy in numerous machine learning tasks. In spite of their great success in terms of accuracy, th…
Online Learning to Rank in Stochastic Click Models
Masrour Zoghi, Tomas Tunys, Mohammad Ghavamzadeh +3
Online learning to rank is a core problem in information retrieval and machine learning. Many provably efficient algorithms have been recently proposed for this problem in specific…